Girish Chowdhary

About

Girish Chowdhary is associated with the Center for Digital Agriculture at the University of Illinois. The center focuses on advancing digital and precision agriculture through research, education, and industry collaboration. The center's initiatives include developing AI-driven tools such as CropWizard, a decision-support service powered by generative AI designed for agricultural professionals, and supporting projects like AI AgriBench to evaluate AI tools in agronomy. The center also offers interdisciplinary educational programs, including a fully online Master’s Degree in Engineering with a concentration in Digital Agriculture, aimed at cultivating expertise in digital agriculture technologies. The Center for Digital Agriculture is engaged in research that spans data collection, storage, transmission, and analysis, with the goal of optimizing various aspects of agriculture such as precision farming, water use, and food manufacturing. Key projects include the development of datasets like PigLife for livestock industry optimization and exploring generative AI applications like CropWizard and CropGPT to enhance decision-making for farmers. The center actively promotes global perspectives on digital and smart agriculture through joint seminar series with institutions like National Taiwan University and hosts events such as the CDA Conference and AgTech Week to foster innovation and knowledge exchange in the field.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Machine Learning
• Computer vision
• Natural resource economics
• Business
• Ecology
• Geography
• Environmental science

Selected publications

• Agricultural robotics research applicable to poultry production: A review

  Computers and Electronics in Agriculture · 2020 · 186 citations

  DOI

• Digital transformation for a sustainable agriculture in the United States: Opportunities and challenges

  Agricultural Economics · 2022 · 78 citations

  Abstract Agriculture faces key challenges of increasing productivity while reducing adverse impacts on the environment. Conventional practices that rely on tillage, inefficient and over‐application of chemicals, and monoculture row cropping are leading to growing resistance of weeds and pests to chemicals, nutrient and sediment run‐off, and declining soil carbon stocks in the United States. Digital technologies and artificial intelligence (AI) technologies are enabling the collection of vast amo…

  PublisherDOI

• Lyapunov-Based Real-Time and Iterative Adjustment of Deep Neural Networks

  IEEE Control Systems Letters · 2021 · 73 citations

  A real-time Deep Neural Network (DNN) adaptive control architecture is developed for general uncertain nonlinear dynamical systems to track a desired time-varying trajectory. A Lyapunov-based method is leveraged to develop adaptation laws for the output-layer weights of a DNN model in real-time while a data-driven supervised learning algorithm is used to update the inner-layer weights of the DNN. Specifically, the output-layer weights of the DNN are estimated using an unsupervised learning algor…

  DOI

• WayFAST: Navigation With Predictive Traversability in the Field

  IEEE Robotics and Automation Letters · 2022 · 62 citations

  Senior authorCorresponding

  We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provi…

  PublisherOA PDFDOI

• A Berry Picking Robot With A Hybrid Soft-Rigid Arm: Design and Task Space Control

  2020 · 23 citations

  We present a hybrid rigid-soft arm and manipulator for performing tasks requiring dexterity and reach in cluttered environments. Our system combines the benefit of the dexterity of a variable length soft manipulator and the rigid support capability of a hard arm. The hard arm positions the extendable soft manipulator close to the target, and the soft arm manipulator navigates the last few centimeters to reach and grab the target. A novel magnetic sensor and reinforcement learning based control i…

  DOI

Recent grants

• NRI: Collaborative Goal and Policy Learning from Human Operators of Construction Co¬-Robots

  NSF · $900k · 2015–2017

Frequent coauthors

• Jonathan P. How

  66 shared

• Eric N. Johnson

  Google (United States)

  38 shared

• Mateus V. Gasparino

  25 shared

• John Vian

  Boeing (Australia)

  21 shared

• John F. Quindlen

  Boeing (Australia)

  19 shared

• Hassan A. Kingravi

  Georgia Institute of Technology

  18 shared

• Nazım Kemal Üre

  18 shared

• Arun N. Sivakumar

  University of Illinois Urbana-Champaign

  17 shared

Similar researchers at University of Illinois Urbana-Champaign

• Yuanhui Zhangh-75
• Vijay Singhh-52
• Kent D. Rauschh-36